TY - JOUR
T1 - Grapevine stem water potential seasonal curves
T2 - response to meteorological conditions, and association to yield and red wine quality
AU - Ohana-Levi, Noa
AU - Munitz, Sarel
AU - Netzer, Yishai
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/11/15
Y1 - 2023/11/15
N2 - Managing grapevine water status is a key component for achieving optimal yield and wine quality. Stem water potential (SWP) is used for determining the drought stress and is associated with physiological, vegetative, and reproductive processes. SWP seasonal curves (SWP-SCs) represent the seasonal stress levels. SWP-SCs may change within and between growing seasons according to environmental and irrigation factors. The objective was to analyze the temporal dynamics of SWP-SCs, their response to meteorological and irrigation conditions, and their impact on yield and wine quality. A dataset was assembled from a six-year experiment (2014-2019) in a ‘Cabernet Sauvignon’ vineyard, including meteorological features, irrigation treatments, field measurements of SWP, yield components (i.e., yield, number of clusters, cluster weight, and berry mass), and wine-quality scores from experimental wines. Between- and within-seasonal variations of SWP-SCs were assessed using ANOVA and discriminant analysis. The effects of meteorology and irrigation on SWP-SCs were quantified using random forest. Clustering of SWP-SCs was conducted using a fuzzy c-means algorithm and the clusters (groups) were analyzed against yield components and wine quality using ANOVA. Our findings show that the mean seasonal SWP values were significantly different among seasons, while SWP values during the weeks proximal to veraison were the major discriminators. Temperature was a strong driver of SWP, followed by relative humidity and precipitation. Three clusters were generated according to stress levels throughout the season. The moderate-severe stress group (Cluster 2) was associated with similar yield components levels compared to the moderately-stressed group (Cluster 1) and significantly higher wine quality components, with a minimal SWP threshold of -1.5 - -1.6 MPa before harvest. The extreme drought stress group (Cluster 3) was associated with significantly lower yield and wine quality. Increasing temperatures and shifting patterns of precipitation are projected to continue excessively, inducing disruptive conditions to the target vine drought stress levels.
AB - Managing grapevine water status is a key component for achieving optimal yield and wine quality. Stem water potential (SWP) is used for determining the drought stress and is associated with physiological, vegetative, and reproductive processes. SWP seasonal curves (SWP-SCs) represent the seasonal stress levels. SWP-SCs may change within and between growing seasons according to environmental and irrigation factors. The objective was to analyze the temporal dynamics of SWP-SCs, their response to meteorological and irrigation conditions, and their impact on yield and wine quality. A dataset was assembled from a six-year experiment (2014-2019) in a ‘Cabernet Sauvignon’ vineyard, including meteorological features, irrigation treatments, field measurements of SWP, yield components (i.e., yield, number of clusters, cluster weight, and berry mass), and wine-quality scores from experimental wines. Between- and within-seasonal variations of SWP-SCs were assessed using ANOVA and discriminant analysis. The effects of meteorology and irrigation on SWP-SCs were quantified using random forest. Clustering of SWP-SCs was conducted using a fuzzy c-means algorithm and the clusters (groups) were analyzed against yield components and wine quality using ANOVA. Our findings show that the mean seasonal SWP values were significantly different among seasons, while SWP values during the weeks proximal to veraison were the major discriminators. Temperature was a strong driver of SWP, followed by relative humidity and precipitation. Three clusters were generated according to stress levels throughout the season. The moderate-severe stress group (Cluster 2) was associated with similar yield components levels compared to the moderately-stressed group (Cluster 1) and significantly higher wine quality components, with a minimal SWP threshold of -1.5 - -1.6 MPa before harvest. The extreme drought stress group (Cluster 3) was associated with significantly lower yield and wine quality. Increasing temperatures and shifting patterns of precipitation are projected to continue excessively, inducing disruptive conditions to the target vine drought stress levels.
KW - SWP modeling
KW - Vitis vinifera
KW - cluster analysis
KW - machine learning
KW - multivariate analysis
KW - vineyards
UR - http://www.scopus.com/inward/record.url?scp=85173991664&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2023.109755
DO - 10.1016/j.agrformet.2023.109755
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AN - SCOPUS:85173991664
SN - 0168-1923
VL - 342
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 109755
ER -